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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

ASTEP user's guide and software documentation

The Algorithm Simulation Test and Evaluation Program (ASTEP) is a modular computer program developed for the purpose of testing and evaluating methods of processing remotely sensed multispectral scanner earth resources data. ASTEP is written in FORTRAND V on the UNIVAC 1110 under the EXEC 8 operating system and may be operated in either a batch or interactive mode. The program currently contains over one hundred subroutines consisting of data classification and display algorithms, statistical analysis algorithms, utility support routines, and feature selection capability. The current program can accept data in LARSC1, LARSC2, ERTS, and Universal formats, and can output processed image or data tapes in Universal format.

Gliniewicz, A. S.↗
NASA NTRS1985

Unsupervised Classification of Global Radar Units on Venus

Characterization of the Venusian surface in terms of its radar properties was accomplished by application of an unsupervised, linear discriminant algorithm to two Pioneer-Venus (PV) Orbiter radar data sets: the RMS-slope (surface roughness) and reflectivity. Both databases were spatially filtered to the same effective resolution of 100 km prior to classification. A recent supervised classification study using these data was based on presupposed morphologic significance of selected data ranges. The knowledge of both Venusian geology and the geologic significance of the radar data is so limited that the data warrant a more unsupervised approach; for this study a linear discriminant classifier was chosen. This approach is purely statistical, thereby removing any observer bias. Statistical significance of the resulting clusters was evaluated by an ancillary program in which an F test utilizing the Mahalanobis' distance.

Kozak, R. C.↗
NASA NTRS2018

Follow-up Imaging of Disk Candidates from the Disk Detective Citizen Science Project: New Discoveries and False Positives in WISE Circumstellar Disk Surveys

The Disk Detective citizen science project aims to find new stars with excess 22 m emission from circumstellar dust in the All WISE data release from the Wide-field Infrared Survey Explorer. We evaluated 261 Disk Detective objects of interest with imaging with the Robo-AO adaptive optics instrument on the 1.5 m telescope at Palomar Observatory and with RetroCam on the 2.5 m du Pont Telescope at Las Campanas Observatory to search for background objects at 0 15–12 separations from each target. Our analysis of these data leads us to reject 7% of targets. Combining this result with statistics from our online image classification efforts implies that at most7.9%±0.2% of All WISE-selected infrared excesses are good disk candidates. Applying our false-positive rates to other surveys, we find that the infrared excess searches of McDonald et al. and Marton et al. all have false-positiverates >70%. Moreover, we find that all 13 disk candidates in Theissen & West with W4 signal-to-noise ratio >3are false positives. We present 244 disk candidates that have survived vetting by follow-up imaging. Of these,213 are newly identified disk systems. Twelve of these are candidate members of comoving pairs based on Gaia astrometry, supporting the hypothesis that warm dust is associated with binary systems. We also note the discovery of 22 m excess around two known members of the Scorpius–Centaurus association, and we identifyknown disk host WISEA J164540.79-310226.6 as a likely Sco-Cen member. Thirty of these disk candidates arecloser than 125 pc (including 26 debris disks), making them good targets for both direct-imaging exoplanetsearches.

Wide-field Infrared Survey Explorer↗
NASA NTRS1981

Development of alternative data analysis techniques for improving the accuracy and specificity of natural resource inventories made with remote sensing data

The applicability of digital LANDSAT data in updating the Minnesota Lake Land Management Information System and in assessing the trophic status of lakes was investigated. Data from various combinations of training, classification, and geometric correction techniques were compared to a photointerpreted reference data set consisting of ground-verified samples and six randomly selected photographs covering approximately one quarter of the study area. For a reconnaissance inventory, the training via polygons selected from aerial photographs with a canonical analysis minimum distance classifier is the most accurate and efficient analysis technique. The applications test project resulted in disappointing thematic classification results per se, but did contribute to the development of mechanisms for implementing a LANDSAT data processing capability in the State Planning Agency on a permanent basis.

Lillesand, T. M.↗
NASA NTRS2013

The Ocean Colour Climate Change Initiative: III. A Round-Robin Comparison on In-Water Bio-Optical Algorithms

Satellite-derived remote-sensing reflectance (Rrs) can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scale for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Using in situ Rrs as input to the models, the performance of eleven semianalytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, is ranked for spectrally-resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking are tested using a Monte-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflects either their immunity to scale errors or instrument noise in Rrs data, or simply that the data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggests that the performance of some semi-analytical algorithms at retrieving chlorophyll is comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also perform with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate-change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.

Phytoplankton↗
DOE OSTI2025

Extending Shared Socioeconomic Pathways to Future Water Supply In-frastructure Scenarios: A Case Study of San Antonio, TX

Datasets supporting findings and visualization behind Okoye and McManamay (2025) Extending Shared Socioeconomic Pathways to Future Water Supply Infrastructure Scenarios: A Case Study of San Antonio, TX. Environmental Research Communications, DOI: 10.57931/2563186 These datasets contains the results of a site selection analysis for municipal water supply planning across multiple Shared Socioeconomic Pathways (SSPs 1–5) and hard scenario classification of water systems in San Antonio, TX. It includes data at the resolution of individual surface water supply sources (COMIDs) and integrates a wide range of hydrologic, socioeconomic, infrastructural, and scenario-based planning variables. Please refer to the README file provided in Files for more details. Descriptions of the datasets are provided below. Dataset(s) Descriptions: Dataset_SSP1_SSP4.xlsx - Contains data used for site selection optimization under SSP1 to SSP4. This dataset was generated based on multi-indicator computations (e.g., WAI, WQI, ERI, WTC, WIC), scenario demand projections, and resource and spatial constraints, excluding new reservoir values. Dataset_SSP5.xlsx - Used for site optimization under SSP5. Unlike Dataset_SSP1_SSP4, this dataset includes new reservoir features with updated calculations of WAI, WTC, and WIC to reflect the added infrastructure and supply potential. hard_classification.xlsx - Provides the scenario classification output for each site. Includes both the initial scenario classification based on Euclidean Distance and adjusted classifications based on 30% change reduction BAU.zip - Zipped folder of .shp files showing spatially optimized water supply sites per SSP under the Business-As-Usual (BAU) water demand strategy LowGW.zip - Zipped folder of .shp files showing optimized site selections under the Low Groundwater strategy

geospatial↗
NASA NTRS1992

Automating sky object classification in astronomical survey images

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomer Observatory Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 10(exp 7) galaxies and 10(exp 8) stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. The size of this data set precludes manual analysis. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3* and O-BTree, two inductive learning techniques, learn classification decision trees from examples. These classifiers will be used to process the rest of the data. This paper gives an overview of the machine learning techniques used, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our approach is well-suited to the problem. The primary benefits of the approach are increased data reduction throughput and consistency of classification. The classification rules which are the product of the inductive learning techniques will form an object, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically cataloged data.

Fayyad, Usama M.↗
NASA NTRS1999

Global Geochemical Variation on the Lunar Surface: A Three-Element Approach

We present a method for displaying the relative abundances of three important elements (Th, Fe, and Ti) on the same map projection of the lunar surface. Using Th-, Fe-, and Ti-elemental abundances from orbital geochemical data and assigning each element a primary color, a false-color map of the lunar surface was created. This approach is similar to the ternary diagram approach presented by Davis and Spudis with some important differences, discussed later. For the present maps, Th abundances were measured by the Lunar Prospector (LP) Gamma-Ray Spectrometer(GRS).The new LPGRS low-altitude dataset was used in this analysis. Iron and Ti weight percentages were based on Clementine spectral reflectance data smoothed to the LP low altitude footprint. This method of presentation was designed to aid in the location and recognition of three principal lunar compositions: ferroan anorthosite (FAN), mare basalts (MB), and the Mg suite/ KREEP-rich rocks on the lunar surface, with special emphasis on the highlands and specific impact basins. In addition to the recognition of these endmember rock compositions, this method is an attempt to examine the relationship between elemental compositions that do not conform readily to previously accepted or observed endmember rocks in various specific regions of interest, including eastern highlands regions centered on 150 deg longitude, and a northern highlands Th-rich region observed. The LP low-altitude data has full width at half-maximum spatial resolution of about 40 km. The Clementine spectral reflectance datasets were adapted using an equal-area, gaussian smoothing routine to this footprint. In addition, these datasets, reported in weight percent of FeO and of Ti02, were adjusted to Fe and Ti weight percentages. Each dataset was then assigned one of the three primary colors: blue for Th, red for Fe, and green for Ti. For each element, the data range was normalized to represent the ratio of each point to the maximum in the dataset. (To view the color table, go to http://cass.jsc.nasa.gov/meetings/moon99/pdf/8033.pdf.) The full range of lunar longitudes is represented, but due to the lack of coverage of the Clementine data for latitudes > 70 deg and <-70 deg, the data for these regions is excluded. The differences between this approach and the ternary diagram approach of Davis and Spudis eliminate some of the uncertainty and ambiguity of the ternary diagram approach. Rather than using a ratio of Th to Ti normalized to CI chondritic ratios, and a ternary diagram with ternary apexes located at specific endmember compositional values, elemental compositions were used independently, eliminating the errors resulting from dividing numbers that can have high uncertainties, especially at low concentration. The three elements used in this method of presentation were chosen for several reasons. One reason for the inclusion of Th in this study is that it is an accurate indicator of KREEP. Iron and Ti concentrations are both low in highland regolith, causing any small fluctuations in Th to stand out very well. In addition, Fe and Ti are good compositional indicators of different mare basalts. Mixed with red for Fe, the green for Ti produces a yellow signal in high-Ti basalts. While universally high in Fe relative to the surrounding highlands, mare basalts have a diverse range of Ti values, making Ti concentration a valuable asset to the classification and identification of different basalt types. Finally, an important constraint in element selection is the availability of the global data, both from LP and Clementine results. Data for Th, Fe, and Ti are among the highest quality of existing lunar remote-sensing data. In addition, LP data for Fe and Ti will become available, enabling these data to be incorporated into the analysis. Using upper-limit values for end member rock compositions calculated from Korotev et al., attempts were made to locate the different endmember compositions of terranes on this diagram. Most strikingly, ferroan anorthosite (Th < and = 0.37 micro g/g; Fe (wt%)< and =2.29; Ti (wt%) < and = 0.22), which should appear as an almost black, reddish color, does not appear on the diagram at any noticeable frequency. Based on this analysis, the suggestion of extensive FAN regions on the lunar surface is not strong, especially at the presently accepted values for Fe and Th. However, to make sure this effect is not due to systematic errors, a thorough investigation of the precision, accuracy, and uncertainties of the Fe, Ti, and Th abundances needs to be carried out, especially at low concentrations. A particular region of interest is an area of high Th concentrations relative to Fe and Ti content north and east of Humboldtianum Crater. First observed by Lawrence et al., this region does not coincide with any visible impact structure and comprises one of the closest approximations to pure blue (high Th, very low Ti and Fe) on the lunar surface. Such an elemental composition does not lend itself readily to classification, and presents something of an anomaly. More detailed analysis of this region is needed to understand its structure and origin. There seems to be a longitudinal asymmetry in the Th concentrations of the highlands regolith. High-Th, low-Ti, and Fe regions are located between 135 deg and 180 deg longitude and between -30 deg and +30 deg latitude. While the Th levels are not high enough to attract attention in a single elemental display, the variation in the abundance of Th relative to Fe and Ti abundances can be clearly seen. The composition that these data suggest is not well represented in the sample return suite. In addition, these regions were largely missed by the Apollo orbital ground tracks, which only covered the outer edge of the areas of interest. The LP orbital Th data represent the first information about the Th concentrations in these regions of the highlands. Additional information contained in original.

Thomsen, D. R.↗
NASA NTRS1976

Snowpack ground truth: Radar test site, Steamboat Springs, Colorado, 8-16 April 1976

Ground-truth data taken at Steamboat Springs, Colorado is presented. Data taken during the period April 8, 1976 - April 16, 1976 included the following: (1) snow depths and densities at selected locations (using a Mount Rose snow tube); (2) snow pits for temperature, density, and liquid water determinations using the freezing calorimetry technique and vertical layer classification; (3) snow walls were also constructed of various cross sections and documented with respect to sizes and snow characteristics; (4) soil moisture at selected locations; and (5) appropriate air temperature and weather data.

Howell, S.↗
NASA NTRSDate not supplied

Onboard Hyperspectral Image Classification via Transfer Learning for Communication-Limited Spacecraft

Employing deep-learning and artificial-intelligence (AI) techniques onboard spacecraft can dramatically improve priority data selection to ensure more effective use of the available downlink. However, deployment of effective deep-learning models requires significant training on the ground, which may not be feasible, due to limited data available in an unexplored environment. Therefore, this research explores building robust classification models for onboard data processing where training data is highly limited using transfer-learning techniques. In this paper, we focus on the use case of hyperspectral imaging for remote sensing, a domain where the high dimensionality of the data from the sensor can rapidly saturate the downlink bandwidth. With this bottleneck, there is an impending need to autonomously and robustly classify data onboard to optimize downlink of high-impact measurements, thus maximizing the scientific utility per bit transmitted to the ground. This paper examines the use of deep neural networks onboard for hyperspectral image classification in a communication-limited scenario to analyze how the models perform with limited training data. The use of transfer learning can ameliorate the issue of poor generalization by transferring features learned from training on a large source dataset for one classification task to the target classification task with limited training data. For two deep-learning models from literature, we compare the accuracy of the models trained using transfer learning to models trained from scratch using a random weight initialization with varying amounts of training data. We demonstrate the feasibility and performance of running inference of the deep-learning models on representative flight-like hardware.

Advanced Avionics, Machine Learning, Data Processi↗
NASA NTRS1977

Utility of correlation techniques in gravity and magnetic interpretation

Internal correspondence uses Poisson's Theorem in a moving-window linear regression analysis between the anomalous first vertical derivative of gravity and total magnetic field reduced to the pole. The regression parameters provide critical information on source characteristics. The correlation coefficient indicates the strength of the relation between magnetics and gravity. Slope value gives delta j/delta sigma estimates of the anomalous source. The intercept furnishes information on anomaly interference. Cluster analysis consists of the classification of subsets of data into groups of similarity based on correlation of selected characteristics of the anomalies. Model studies are used to illustrate implementation and interpretation procedures of these methods, particularly internal correspondence. Analysis of the results of applying these methods to data from the midcontinent and a transcontinental profile shows they can be useful in identifying crustal provinces, providing information on horizontal and vertical variations of physical properties over province size zones, validating long wavelength anomalies, and isolating geomagnetic field removal problems.

Chandler, V. W.↗
NASA NTRS1979

Large Area Crop Inventory Experiment (LACIE). Development of procedure M for multicrop inventory, with tests of a spring-wheat configuration

The author has identified the following significant results. An outgrowth of research and development activities in support of LACIE was a multicrop area estimation procedure, Procedure M. This procedure was a flexible, modular system that could be operated within the LACIE framework. Its distinctive features were refined preprocessing (including spatially varying correction for atmospheric haze), definition of field like spatial features for labeling, spectral stratification, and unbiased selection of samples to label and crop area estimation without conventional maximum likelihood classification.

Horvath, R.↗
NASA NTRS1979

Identification and area estimation of agricultural crops by computer classification of Landsat MSS data

Landsat Multispectral Scanner (MSS) data covering a three-county area in northern Illinois were classified using computer-aided techniques as corn, soybeans, or 'other.' Recognition of test fields was 80% accurate. County estimates of the area of corn and soybeans agreed closely with those made by the USDA. Results of the use of a priori information in classification, techniques to produce unbiased area estimates, and the use of temporal and spatial features for classification are discussed. The extendability, variability, and size of training sets, wavelength band selection, and spectral characteristics of crops were also investigated.

Bauer, M. E.↗
NASA NTRS1980

Taxonomic classification of world map units in crop producing areas of Argentina and Brazil with representative US soil series and major land resource areas in which they occur

The most probable current U.S. taxonomic classification of the soils estimated to dominate world soil map units (WSM)) in selected crop producing states of Argentina and Brazil are presented. Representative U.S. soil series the units are given. The map units occurring in each state are listed with areal extent and major U.S. land resource areas in which similar soils most probably occur. Soil series sampled in LARS Technical Report 111579 and major land resource areas in which they occur with corresponding similar WSM units at the taxonomic subgroup levels are given.

Huckle, H. F.↗
NASA NTRS1993

Age discrimination among eruptives of Menengai Caldera, Kenya, using vegetation parameters from satellite imagery

Results are presented of an investigation to determine the degree to which digitally processed Landsat TM imagery can be used to discriminate among vegetated lava flows of different ages in the Menengai Caldera, Kenya. A selective series of five images, consisting of a color-coded Landsat 5 classification and four color composites, are compared with geologic maps. The most recent of more than 70 postcaldera flows within the caldera are trachytes, which are variably covered by shrubs and subsidiary grasses. Soil development evolves as a function of time, and as such supports a changing plant community. Progressively older flows exhibit the increasing dominance of grasses over bushes. The Landsat images correlated well with geologic maps, but the two mapped age classes could be further subdivided on the basis of different vegetation communities. It is concluded that field maps can be modified, and in some cases corrected by use of such imagery, and that digitally enhanced Landsat imagery can be a useful aid to field mapping in similar terrains.

Blodget, Herbert W.↗